An anomaly may be characterized as something that deviates from the norm (or something abnormal). In some cases, an anomaly in data can be indicative of a surprising or interesting event. For example, eyewitnesses of a car accident may use their mobile devices to post about the car accident to a social media service shortly after the accident. The data that is collected by the social media service—in this case, user posts—can include information about the car accident, and the data may therefore be anomalous in that it deviates from the norm. Detecting such an anomaly in data may lead to useful insights, such as identifying the car accident before it is reported by major news outlets. Accordingly, anomaly detection lends itself to a variety of downstream applications, and improvements in anomaly detection are needed.